Overview

Dataset statistics

Number of variables19
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory147.6 B

Variable types

Text3
Boolean1
DateTime1
Numeric12
Categorical2

Alerts

danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudnessHigh correlation
loudness is highly overall correlated with energyHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
time_signature is highly imbalanced (53.1%)Imbalance
track_name has unique valuesUnique
loudness has unique valuesUnique
tempo has unique valuesUnique
duration_ms has unique valuesUnique
instrumentalness has 14 (28.0%) zerosZeros
key has 4 (8.0%) zerosZeros

Reproduction

Analysis started2024-02-15 13:47:06.374675
Analysis finished2024-02-15 13:47:44.544399
Duration38.17 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct41
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2024-02-15T13:47:44.826471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length18
Median length13
Mean length9.66
Min length3

Characters and Unicode

Total characters483
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)70.0%

Sample

1st rowMiley Cyrus
2nd rowSZA
3rd rowHarry Styles
4th rowJung Kook
5th rowEslabon Armado
ValueCountFrequency (%)
the 6
 
7.1%
weeknd 4
 
4.8%
taylor 3
 
3.6%
swift 3
 
3.6%
bizarrap 2
 
2.4%
fifty 2
 
2.4%
d4vd 2
 
2.4%
david 2
 
2.4%
sza 2
 
2.4%
bunny 2
 
2.4%
Other values (55) 56
66.7%
2024-02-15T13:47:45.666231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 44
 
9.1%
a 36
 
7.5%
34
 
7.0%
n 30
 
6.2%
o 26
 
5.4%
r 26
 
5.4%
i 22
 
4.6%
d 18
 
3.7%
l 17
 
3.5%
t 15
 
3.1%
Other values (40) 215
44.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 339
70.2%
Uppercase Letter 107
 
22.2%
Space Separator 34
 
7.0%
Decimal Number 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 44
13.0%
a 36
 
10.6%
n 30
 
8.8%
o 26
 
7.7%
r 26
 
7.7%
i 22
 
6.5%
d 18
 
5.3%
l 17
 
5.0%
t 15
 
4.4%
h 13
 
3.8%
Other values (14) 92
27.1%
Uppercase Letter
ValueCountFrequency (%)
T 15
14.0%
S 10
 
9.3%
B 7
 
6.5%
F 6
 
5.6%
M 6
 
5.6%
A 6
 
5.6%
Y 5
 
4.7%
R 5
 
4.7%
W 5
 
4.7%
O 5
 
4.7%
Other values (13) 37
34.6%
Space Separator
ValueCountFrequency (%)
34
100.0%
Decimal Number
ValueCountFrequency (%)
4 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 446
92.3%
Common 37
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 44
 
9.9%
a 36
 
8.1%
n 30
 
6.7%
o 26
 
5.8%
r 26
 
5.8%
i 22
 
4.9%
d 18
 
4.0%
l 17
 
3.8%
t 15
 
3.4%
T 15
 
3.4%
Other values (37) 197
44.2%
Common
ValueCountFrequency (%)
34
91.9%
4 2
 
5.4%
, 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 44
 
9.1%
a 36
 
7.5%
34
 
7.0%
n 30
 
6.2%
o 26
 
5.4%
r 26
 
5.4%
i 22
 
4.6%
d 18
 
3.7%
l 17
 
3.5%
t 15
 
3.1%
Other values (40) 215
44.5%

track_name
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2024-02-15T13:47:46.179737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length56
Median length33.5
Mean length15.34
Min length3

Characters and Unicode

Total characters767
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowFlowers
2nd rowKill Bill
3rd rowAs It Was
4th rowSeven (feat. Latto) (Explicit Ver.)
5th rowElla Baila Sola
ValueCountFrequency (%)
5
 
3.4%
with 5
 
3.4%
you 4
 
2.7%
feat 3
 
2.1%
i 3
 
2.1%
sessions 2
 
1.4%
em 2
 
1.4%
the 2
 
1.4%
vol 2
 
1.4%
music 2
 
1.4%
Other values (108) 116
79.5%
2024-02-15T13:47:47.047362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
96
 
12.5%
e 60
 
7.8%
i 50
 
6.5%
o 44
 
5.7%
a 43
 
5.6%
n 32
 
4.2%
r 31
 
4.0%
t 31
 
4.0%
l 27
 
3.5%
s 20
 
2.6%
Other values (51) 333
43.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 467
60.9%
Uppercase Letter 147
 
19.2%
Space Separator 96
 
12.5%
Other Punctuation 18
 
2.3%
Decimal Number 16
 
2.1%
Close Punctuation 9
 
1.2%
Open Punctuation 9
 
1.2%
Dash Punctuation 5
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60
12.8%
i 50
10.7%
o 44
 
9.4%
a 43
 
9.2%
n 32
 
6.9%
r 31
 
6.6%
t 31
 
6.6%
l 27
 
5.8%
s 20
 
4.3%
h 19
 
4.1%
Other values (14) 110
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 17
 
11.6%
B 14
 
9.5%
A 10
 
6.8%
L 10
 
6.8%
E 9
 
6.1%
M 8
 
5.4%
W 7
 
4.8%
G 7
 
4.8%
Y 7
 
4.8%
C 7
 
4.8%
Other values (13) 51
34.7%
Other Punctuation
ValueCountFrequency (%)
. 9
50.0%
' 4
22.2%
, 2
 
11.1%
: 2
 
11.1%
& 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 5
31.2%
0 4
25.0%
2 3
18.8%
5 3
18.8%
3 1
 
6.2%
Space Separator
ValueCountFrequency (%)
96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 614
80.1%
Common 153
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60
 
9.8%
i 50
 
8.1%
o 44
 
7.2%
a 43
 
7.0%
n 32
 
5.2%
r 31
 
5.0%
t 31
 
5.0%
l 27
 
4.4%
s 20
 
3.3%
h 19
 
3.1%
Other values (37) 257
41.9%
Common
ValueCountFrequency (%)
96
62.7%
. 9
 
5.9%
) 9
 
5.9%
( 9
 
5.9%
- 5
 
3.3%
1 5
 
3.3%
' 4
 
2.6%
0 4
 
2.6%
2 3
 
2.0%
5 3
 
2.0%
Other values (4) 6
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 767
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96
 
12.5%
e 60
 
7.8%
i 50
 
6.5%
o 44
 
5.7%
a 43
 
5.6%
n 32
 
4.2%
r 31
 
4.0%
t 31
 
4.0%
l 27
 
3.5%
s 20
 
2.6%
Other values (51) 333
43.4%
Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size178.0 B
False
28 
True
22 
ValueCountFrequency (%)
False 28
56.0%
True 22
44.0%
2024-02-15T13:47:47.354798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Minimum2004-11-12 00:00:00
Maximum2023-11-10 00:00:00
2024-02-15T13:47:47.577635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:47.847066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

genres
Text

Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2024-02-15T13:47:48.198226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length103
Median length65
Mean length37.58
Min length7

Characters and Unicode

Total characters1879
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)56.0%

Sample

1st row['pop']
2nd row['pop', 'r&b', 'rap']
3rd row['pop']
4th row['k-pop']
5th row['corrido', 'corridos tumbados', 'sad sierreno', 'sierreno']
ValueCountFrequency (%)
pop 41
18.2%
latino 20
 
8.9%
reggaeton 13
 
5.8%
trap 12
 
5.3%
urbano 11
 
4.9%
canadian 8
 
3.6%
r&b 7
 
3.1%
contemporary 6
 
2.7%
rock 6
 
2.7%
hip 6
 
2.7%
Other values (45) 95
42.2%
2024-02-15T13:47:48.856107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 252
13.4%
o 176
 
9.4%
175
 
9.3%
p 133
 
7.1%
a 130
 
6.9%
r 123
 
6.5%
n 121
 
6.4%
e 92
 
4.9%
i 85
 
4.5%
, 76
 
4.0%
Other values (22) 516
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1259
67.0%
Other Punctuation 336
 
17.9%
Space Separator 175
 
9.3%
Open Punctuation 50
 
2.7%
Close Punctuation 50
 
2.7%
Dash Punctuation 9
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 176
14.0%
p 133
10.6%
a 130
10.3%
r 123
9.8%
n 121
9.6%
e 92
 
7.3%
i 85
 
6.8%
t 72
 
5.7%
g 48
 
3.8%
c 41
 
3.3%
Other values (14) 238
18.9%
Other Punctuation
ValueCountFrequency (%)
' 252
75.0%
, 76
 
22.6%
& 7
 
2.1%
: 1
 
0.3%
Space Separator
ValueCountFrequency (%)
175
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 50
100.0%
Close Punctuation
ValueCountFrequency (%)
] 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1259
67.0%
Common 620
33.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 176
14.0%
p 133
10.6%
a 130
10.3%
r 123
9.8%
n 121
9.6%
e 92
 
7.3%
i 85
 
6.8%
t 72
 
5.7%
g 48
 
3.8%
c 41
 
3.3%
Other values (14) 238
18.9%
Common
ValueCountFrequency (%)
' 252
40.6%
175
28.2%
, 76
 
12.3%
[ 50
 
8.1%
] 50
 
8.1%
- 9
 
1.5%
& 7
 
1.1%
: 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 252
13.4%
o 176
 
9.4%
175
 
9.3%
p 133
 
7.1%
a 130
 
6.9%
r 123
 
6.5%
n 121
 
6.4%
e 92
 
4.9%
i 85
 
4.5%
, 76
 
4.0%
Other values (22) 516
27.5%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66258
Minimum0.445
Maximum0.911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:49.156792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.445
5-th percentile0.4992
Q10.5595
median0.6475
Q30.7765
95-th percentile0.86175
Maximum0.911
Range0.466
Interquartile range (IQR)0.217

Descriptive statistics

Standard deviation0.1230895
Coefficient of variation (CV)0.18577303
Kurtosis-0.98730294
Mean0.66258
Median Absolute Deviation (MAD)0.096
Skewness0.21424574
Sum33.129
Variance0.015151024
MonotonicityNot monotonic
2024-02-15T13:47:49.446766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.571 3
 
6.0%
0.783 2
 
4.0%
0.637 2
 
4.0%
0.706 1
 
2.0%
0.559 1
 
2.0%
0.551 1
 
2.0%
0.708 1
 
2.0%
0.621 1
 
2.0%
0.901 1
 
2.0%
0.859 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
0.445 1
2.0%
0.464 1
2.0%
0.492 1
2.0%
0.508 1
2.0%
0.511 1
2.0%
0.514 1
2.0%
0.515 1
2.0%
0.52 1
2.0%
0.538 1
2.0%
0.551 1
2.0%
ValueCountFrequency (%)
0.911 1
2.0%
0.901 1
2.0%
0.864 1
2.0%
0.859 1
2.0%
0.835 1
2.0%
0.812 1
2.0%
0.804 1
2.0%
0.799 1
2.0%
0.79 1
2.0%
0.784 1
2.0%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51074
Minimum0.131
Maximum0.893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:49.726801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.131
5-th percentile0.1873
Q10.344
median0.507
Q30.6545
95-th percentile0.85385
Maximum0.893
Range0.762
Interquartile range (IQR)0.3105

Descriptive statistics

Standard deviation0.21234577
Coefficient of variation (CV)0.415761
Kurtosis-0.91993792
Mean0.51074
Median Absolute Deviation (MAD)0.161
Skewness0.09178166
Sum25.537
Variance0.045090727
MonotonicityNot monotonic
2024-02-15T13:47:50.013081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.726 2
 
4.0%
0.607 2
 
4.0%
0.632 1
 
2.0%
0.334 1
 
2.0%
0.478 1
 
2.0%
0.153 1
 
2.0%
0.342 1
 
2.0%
0.55 1
 
2.0%
0.399 1
 
2.0%
0.672 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.131 1
2.0%
0.153 1
2.0%
0.172 1
2.0%
0.206 1
2.0%
0.216 1
2.0%
0.223 1
2.0%
0.25 1
2.0%
0.254 1
2.0%
0.299 1
2.0%
0.304 1
2.0%
ValueCountFrequency (%)
0.893 1
2.0%
0.872 1
2.0%
0.857 1
2.0%
0.85 1
2.0%
0.834 1
2.0%
0.825 1
2.0%
0.811 1
2.0%
0.786 1
2.0%
0.739 1
2.0%
0.726 2
4.0%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65988
Minimum0.417
Maximum0.965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:50.322066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.417
5-th percentile0.46025
Q10.55675
median0.678
Q30.7365
95-th percentile0.81835
Maximum0.965
Range0.548
Interquartile range (IQR)0.17975

Descriptive statistics

Standard deviation0.12149925
Coefficient of variation (CV)0.18412324
Kurtosis-0.45252059
Mean0.65988
Median Absolute Deviation (MAD)0.09
Skewness-0.071819044
Sum32.994
Variance0.014762067
MonotonicityNot monotonic
2024-02-15T13:47:50.604917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.73 2
 
4.0%
0.678 2
 
4.0%
0.691 1
 
2.0%
0.807 1
 
2.0%
0.458 1
 
2.0%
0.675 1
 
2.0%
0.593 1
 
2.0%
0.55 1
 
2.0%
0.737 1
 
2.0%
0.782 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.417 1
2.0%
0.43 1
2.0%
0.458 1
2.0%
0.463 1
2.0%
0.479 1
2.0%
0.5 1
2.0%
0.525 1
2.0%
0.532 1
2.0%
0.537 1
2.0%
0.544 1
2.0%
ValueCountFrequency (%)
0.965 1
2.0%
0.831 1
2.0%
0.826 1
2.0%
0.809 1
2.0%
0.807 1
2.0%
0.802 1
2.0%
0.799 1
2.0%
0.797 1
2.0%
0.782 1
2.0%
0.771 1
2.0%

loudness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.01244
Minimum-10.613
Maximum-2.81
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size528.0 B
2024-02-15T13:47:50.885553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-10.613
5-th percentile-9.3171
Q1-7.126
median-5.639
Q3-4.877
95-th percentile-3.6048
Maximum-2.81
Range7.803
Interquartile range (IQR)2.249

Descriptive statistics

Standard deviation1.7844431
Coefficient of variation (CV)-0.29679183
Kurtosis-0.085631736
Mean-6.01244
Median Absolute Deviation (MAD)1.046
Skewness-0.6290975
Sum-300.622
Variance3.1842371
MonotonicityNot monotonic
2024-02-15T13:47:51.177601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.775 1
 
2.0%
-5.421 1
 
2.0%
-5.456 1
 
2.0%
-4.801 1
 
2.0%
-5.339 1
 
2.0%
-4.045 1
 
2.0%
-5.548 1
 
2.0%
-6.713 1
 
2.0%
-4.79 1
 
2.0%
-7.683 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
-10.613 1
2.0%
-9.475 1
2.0%
-9.345 1
2.0%
-9.283 1
2.0%
-9.222 1
2.0%
-8.532 1
2.0%
-8.332 1
2.0%
-8.254 1
2.0%
-7.683 1
2.0%
-7.594 1
2.0%
ValueCountFrequency (%)
-2.81 1
2.0%
-3.547 1
2.0%
-3.549 1
2.0%
-3.673 1
2.0%
-3.798 1
2.0%
-4.02 1
2.0%
-4.045 1
2.0%
-4.067 1
2.0%
-4.089 1
2.0%
-4.185 1
2.0%

acousticness
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2533322
Minimum0.00146
Maximum0.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:51.494804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.00146
5-th percentile0.0033845
Q10.086275
median0.1645
Q30.4055
95-th percentile0.6851
Maximum0.83
Range0.82854
Interquartile range (IQR)0.319225

Descriptive statistics

Standard deviation0.22218825
Coefficient of variation (CV)0.87706281
Kurtosis0.064786108
Mean0.2533322
Median Absolute Deviation (MAD)0.1137
Skewness0.97659295
Sum12.66661
Variance0.049367619
MonotonicityNot monotonic
2024-02-15T13:47:51.772355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.213 2
 
4.0%
0.141 2
 
4.0%
0.00146 1
 
2.0%
0.653 1
 
2.0%
0.777 1
 
2.0%
0.0739 1
 
2.0%
0.0125 1
 
2.0%
0.00302 1
 
2.0%
0.145 1
 
2.0%
0.255 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.00146 1
2.0%
0.0025 1
2.0%
0.00302 1
2.0%
0.00383 1
2.0%
0.011 1
2.0%
0.0125 1
2.0%
0.0492 1
2.0%
0.0495 1
2.0%
0.0521 1
2.0%
0.0584 1
2.0%
ValueCountFrequency (%)
0.83 1
2.0%
0.777 1
2.0%
0.695 1
2.0%
0.673 1
2.0%
0.653 1
2.0%
0.583 1
2.0%
0.544 1
2.0%
0.483 1
2.0%
0.467 1
2.0%
0.453 1
2.0%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020541799
Minimum0
Maximum0.634
Zeros14
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:52.038422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1995 × 10-5
Q30.00026325
95-th percentile0.09405
Maximum0.634
Range0.634
Interquartile range (IQR)0.00026325

Descriptive statistics

Standard deviation0.093655365
Coefficient of variation (CV)4.5592581
Kurtosis39.462494
Mean0.020541799
Median Absolute Deviation (MAD)1.1995 × 10-5
Skewness6.0868357
Sum1.02709
Variance0.0087713273
MonotonicityNot monotonic
2024-02-15T13:47:52.716951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 14
28.0%
6.99 × 10-51
 
2.0%
9.54 × 10-51
 
2.0%
0.162 1
 
2.0%
0.00162 1
 
2.0%
0.033 1
 
2.0%
0.000237 1
 
2.0%
3.85 × 10-61
 
2.0%
0.0177 1
 
2.0%
1.64 × 10-61
 
2.0%
Other values (27) 27
54.0%
ValueCountFrequency (%)
0 14
28.0%
1.21 × 10-61
 
2.0%
1.64 × 10-61
 
2.0%
1.8 × 10-61
 
2.0%
1.98 × 10-61
 
2.0%
2.96 × 10-61
 
2.0%
3.07 × 10-61
 
2.0%
3.85 × 10-61
 
2.0%
4.15 × 10-61
 
2.0%
6.35 × 10-61
 
2.0%
ValueCountFrequency (%)
0.634 1
2.0%
0.162 1
2.0%
0.144 1
2.0%
0.033 1
2.0%
0.022 1
2.0%
0.0177 1
2.0%
0.00805 1
2.0%
0.00162 1
2.0%
0.00128 1
2.0%
0.00101 1
2.0%

liveness
Real number (ℝ)

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16128
Minimum0.0232
Maximum0.371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:52.987463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.077445
Q10.094675
median0.1215
Q30.227
95-th percentile0.3416
Maximum0.371
Range0.3478
Interquartile range (IQR)0.132325

Descriptive statistics

Standard deviation0.091988378
Coefficient of variation (CV)0.57036445
Kurtosis-0.3740725
Mean0.16128
Median Absolute Deviation (MAD)0.0309
Skewness0.95268825
Sum8.064
Variance0.0084618616
MonotonicityNot monotonic
2024-02-15T13:47:53.270744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.123 2
 
4.0%
0.142 2
 
4.0%
0.112 2
 
4.0%
0.0232 1
 
2.0%
0.11 1
 
2.0%
0.25 1
 
2.0%
0.202 1
 
2.0%
0.0955 1
 
2.0%
0.23 1
 
2.0%
0.0992 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
0.0232 1
2.0%
0.0546 1
2.0%
0.0756 1
2.0%
0.0797 1
2.0%
0.0822 1
2.0%
0.0837 1
2.0%
0.0897 1
2.0%
0.0915 1
2.0%
0.093 1
2.0%
0.0933 1
2.0%
ValueCountFrequency (%)
0.371 1
2.0%
0.357 1
2.0%
0.347 1
2.0%
0.335 1
2.0%
0.322 1
2.0%
0.311 1
2.0%
0.301 1
2.0%
0.291 1
2.0%
0.274 1
2.0%
0.271 1
2.0%

speechiness
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083594
Minimum0.0256
Maximum0.333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:53.588665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0256
5-th percentile0.029185
Q10.03895
median0.05095
Q30.0812
95-th percentile0.27655
Maximum0.333
Range0.3074
Interquartile range (IQR)0.04225

Descriptive statistics

Standard deviation0.077912872
Coefficient of variation (CV)0.93203905
Kurtosis2.9838382
Mean0.083594
Median Absolute Deviation (MAD)0.017
Skewness1.9903827
Sum4.1797
Variance0.0060704157
MonotonicityNot monotonic
2024-02-15T13:47:53.854323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.044 2
 
4.0%
0.0633 1
 
2.0%
0.0598 1
 
2.0%
0.0389 1
 
2.0%
0.0322 1
 
2.0%
0.0286 1
 
2.0%
0.0436 1
 
2.0%
0.289 1
 
2.0%
0.159 1
 
2.0%
0.194 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.0256 1
2.0%
0.0258 1
2.0%
0.0286 1
2.0%
0.0299 1
2.0%
0.0322 1
2.0%
0.0331 1
2.0%
0.0332 1
2.0%
0.0335 1
2.0%
0.0336 1
2.0%
0.0343 1
2.0%
ValueCountFrequency (%)
0.333 1
2.0%
0.289 1
2.0%
0.277 1
2.0%
0.276 1
2.0%
0.266 1
2.0%
0.194 1
2.0%
0.159 1
2.0%
0.157 1
2.0%
0.132 1
2.0%
0.114 1
2.0%

key
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5
Minimum0
Maximum11
Zeros4
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:54.091514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5225223
Coefficient of variation (CV)0.70450446
Kurtosis-1.1276041
Mean5
Median Absolute Deviation (MAD)3
Skewness0.25096961
Sum250
Variance12.408163
MonotonicityNot monotonic
2024-02-15T13:47:54.297785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7
14.0%
2 7
14.0%
6 5
10.0%
11 5
10.0%
5 5
10.0%
4 5
10.0%
7 5
10.0%
0 4
8.0%
9 4
8.0%
10 2
 
4.0%
ValueCountFrequency (%)
0 4
8.0%
1 7
14.0%
2 7
14.0%
4 5
10.0%
5 5
10.0%
6 5
10.0%
7 5
10.0%
8 1
 
2.0%
9 4
8.0%
10 2
 
4.0%
ValueCountFrequency (%)
11 5
10.0%
10 2
 
4.0%
9 4
8.0%
8 1
 
2.0%
7 5
10.0%
6 5
10.0%
5 5
10.0%
4 5
10.0%
2 7
14.0%
1 7
14.0%

tempo
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.0695
Minimum67.033
Maximum203.759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:54.657150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum67.033
5-th percentile80.92905
Q197.963
median124.9835
Q3138.05975
95-th percentile177.2542
Maximum203.759
Range136.726
Interquartile range (IQR)40.09675

Descriptive statistics

Standard deviation31.396557
Coefficient of variation (CV)0.25305621
Kurtosis-0.16276053
Mean124.0695
Median Absolute Deviation (MAD)23.3995
Skewness0.41010693
Sum6203.475
Variance985.74379
MonotonicityNot monotonic
2024-02-15T13:47:55.082434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118.048 1
 
2.0%
96.006 1
 
2.0%
203.759 1
 
2.0%
94.467 1
 
2.0%
101.19 1
 
2.0%
91.986 1
 
2.0%
128.033 1
 
2.0%
98.002 1
 
2.0%
100.065 1
 
2.0%
99.974 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
67.033 1
2.0%
67.528 1
2.0%
78.558 1
2.0%
83.827 1
2.0%
84.039 1
2.0%
88.98 1
2.0%
91.986 1
2.0%
92.005 1
2.0%
94.467 1
2.0%
96.006 1
2.0%
ValueCountFrequency (%)
203.759 1
2.0%
186.003 1
2.0%
179.974 1
2.0%
173.93 1
2.0%
171.005 1
2.0%
169.994 1
2.0%
169.922 1
2.0%
167.968 1
2.0%
147.989 1
2.0%
143.992 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
1
28 
0
22 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 28
56.0%
0 22
44.0%

Length

2024-02-15T13:47:55.551175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T13:47:55.989025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 28
56.0%
0 22
44.0%

Most occurring characters

ValueCountFrequency (%)
1 28
56.0%
0 22
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 28
56.0%
0 22
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 28
56.0%
0 22
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 28
56.0%
0 22
44.0%

duration_ms
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200458.1
Minimum131013
Maximum272373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:56.344777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum131013
5-th percentile150943.45
Q1176936.75
median199740
Q3228219.75
95-th percentile247880
Maximum272373
Range141360
Interquartile range (IQR)51283

Descriptive statistics

Standard deviation32580.811
Coefficient of variation (CV)0.16253177
Kurtosis-0.46778804
Mean200458.1
Median Absolute Deviation (MAD)23901
Skewness-0.027874841
Sum10022905
Variance1.0615092 × 109
MonotonicityNot monotonic
2024-02-15T13:47:56.853754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200600 1
 
2.0%
231827 1
 
2.0%
163855 1
 
2.0%
209260 1
 
2.0%
176440 1
 
2.0%
197920 1
 
2.0%
198938 1
 
2.0%
196600 1
 
2.0%
195987 1
 
2.0%
230480 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
131013 1
2.0%
132631 1
2.0%
148486 1
2.0%
153947 1
2.0%
156943 1
2.0%
162638 1
2.0%
163855 1
2.0%
165671 1
2.0%
167303 1
2.0%
174253 1
2.0%
ValueCountFrequency (%)
272373 1
2.0%
260253 1
2.0%
250760 1
2.0%
244360 1
2.0%
242485 1
2.0%
240400 1
2.0%
239318 1
2.0%
234353 1
2.0%
232857 1
2.0%
231827 1
2.0%

time_signature
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
4
45 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 45
90.0%
3 5
 
10.0%

Length

2024-02-15T13:47:57.192624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T13:47:57.423872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 45
90.0%
3 5
 
10.0%

Most occurring characters

ValueCountFrequency (%)
4 45
90.0%
3 5
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 45
90.0%
3 5
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 45
90.0%
3 5
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 45
90.0%
3 5
 
10.0%

popularity
Real number (ℝ)

Distinct21
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.1
Minimum72
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-02-15T13:47:57.614587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile76.9
Q185
median89
Q392.75
95-th percentile95
Maximum99
Range27
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation5.6901275
Coefficient of variation (CV)0.064587145
Kurtosis0.4881448
Mean88.1
Median Absolute Deviation (MAD)4
Skewness-0.72591601
Sum4405
Variance32.377551
MonotonicityNot monotonic
2024-02-15T13:47:57.892062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
93 6
12.0%
85 5
 
10.0%
84 4
 
8.0%
90 4
 
8.0%
92 4
 
8.0%
95 3
 
6.0%
87 3
 
6.0%
91 3
 
6.0%
94 2
 
4.0%
83 2
 
4.0%
Other values (11) 14
28.0%
ValueCountFrequency (%)
72 1
 
2.0%
75 1
 
2.0%
76 1
 
2.0%
78 1
 
2.0%
81 1
 
2.0%
82 1
 
2.0%
83 2
 
4.0%
84 4
8.0%
85 5
10.0%
86 2
 
4.0%
ValueCountFrequency (%)
99 1
 
2.0%
96 1
 
2.0%
95 3
6.0%
94 2
 
4.0%
93 6
12.0%
92 4
8.0%
91 3
6.0%
90 4
8.0%
89 2
 
4.0%
88 2
 
4.0%

Interactions

2024-02-15T13:47:39.868978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:07.270129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:10.175548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:12.950767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:15.743701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:19.405126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:21.981480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:24.981601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:27.759256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:31.352206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:34.065898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:37.117879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:40.085801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:07.862166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:10.386482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:13.168624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:15.961334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:19.608160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:22.192751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:25.195075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:27.986640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:31.699862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:34.268730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:37.330738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:40.311951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:08.085349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:10.618384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:13.395286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:16.311991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:19.832220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:22.427662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:25.454527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:28.236162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:31.931804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:34.515289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:37.578887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:40.543776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:08.279472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:10.856883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:13.598916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:16.662567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:20.032399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:22.647955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:25.689697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:28.477430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:32.130340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:34.722259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:37.784270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:40.778264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:08.498909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:11.116568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:13.821734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:17.015889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:20.281378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:22.887604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:25.933527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:28.740163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:32.371771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:34.975497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:38.033527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:40.994731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:08.684036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:11.339369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:14.042026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:17.386423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:20.476868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:23.098061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:26.147380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:29.034530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:32.572366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:35.180050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:38.232810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:41.233100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:08.905736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:11.575254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:14.254601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:17.709150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:20.693825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:23.332021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:26.379533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:29.373555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:32.801516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:35.409667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:38.467034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:41.454391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:09.129521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:11.796779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:14.461626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:18.016292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:20.912058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:23.575145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:26.627314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:29.745932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:33.015831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:35.642615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:38.681306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:41.678629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:09.342880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:12.033300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:14.908934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:18.366864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:21.119787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:23.792517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:26.845287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:30.015436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:33.219703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:35.862286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:38.909001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:41.986815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:09.535537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:12.253222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:15.108648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:18.685591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:21.333868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:24.007849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:27.060850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:30.361140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:33.415223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:36.060426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:39.162502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:42.332066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:09.751527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:12.498682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:15.323124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:18.956034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:21.538125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:24.220599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:27.299992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:30.700392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:33.632712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:36.293018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:39.384139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:42.685595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:09.975991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:12.730921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:15.544016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:19.177509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:21.772123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:24.459642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:27.550317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:31.034956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:33.865350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:36.530607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-15T13:47:39.616869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-15T13:47:58.124689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnessis_explicitkeylivenessloudnessmodepopularityspeechinesstempotime_signaturevalence
acousticness1.000-0.1340.008-0.332-0.2020.2540.2040.023-0.1570.000-0.161-0.3250.0370.1830.043
danceability-0.1341.000-0.0470.248-0.1060.0000.110-0.1820.2410.341-0.2550.341-0.0680.0000.543
duration_ms0.008-0.0471.000-0.221-0.1680.106-0.235-0.2240.0850.1220.0050.240-0.1510.073-0.330
energy-0.3320.248-0.2211.0000.3000.0690.359-0.0330.6010.295-0.110-0.0950.1080.0000.407
instrumentalness-0.202-0.106-0.1680.3001.0000.129-0.032-0.053-0.0530.1290.051-0.2940.0310.189-0.025
is_explicit0.2540.0000.1060.0690.1291.0000.048-0.1730.1370.000-0.0910.4590.1540.0000.013
key0.2040.110-0.2350.359-0.0320.0481.0000.4040.2340.000-0.178-0.2180.1910.0000.276
liveness0.023-0.182-0.224-0.033-0.053-0.1730.4041.000-0.1170.200-0.162-0.204-0.0810.000-0.095
loudness-0.1570.2410.0850.601-0.0530.1370.234-0.1171.0000.000-0.0460.0470.0030.0000.133
mode0.0000.3410.1220.2950.1290.0000.0000.2000.0001.0000.239-0.056-0.1200.000-0.135
popularity-0.161-0.2550.005-0.1100.051-0.091-0.178-0.162-0.0460.2391.0000.0140.0700.494-0.062
speechiness-0.3250.3410.240-0.095-0.2940.459-0.218-0.2040.047-0.0560.0141.0000.1230.0000.118
tempo0.037-0.068-0.1510.1080.0310.1540.191-0.0810.003-0.1200.0700.1231.0000.3200.131
time_signature0.1830.0000.0730.0000.1890.0000.0000.0000.0000.0000.4940.0000.3201.000-0.122
valence0.0430.543-0.3300.407-0.0250.0130.276-0.0950.133-0.135-0.0620.1180.131-0.1221.000

Missing values

2024-02-15T13:47:43.246171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-15T13:47:44.170065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

artist_nametrack_nameis_explicitalbum_release_dategenresdanceabilityvalenceenergyloudnessacousticnessinstrumentalnesslivenessspeechinesskeytempomodeduration_mstime_signaturepopularity
0Miley CyrusFlowersFalse2023-08-18['pop']0.7060.6320.691-4.7750.05840.0000700.02320.06330118.0481200600494
1SZAKill BillFalse2022-12-08['pop', 'r&b', 'rap']0.6440.4180.735-5.7470.05210.1440000.16100.0391888.9801153947486
2Harry StylesAs It WasFalse2022-05-20['pop']0.5200.6620.731-5.3380.34200.0010100.31100.05576173.9300167303495
3Jung KookSeven (feat. Latto) (Explicit Ver.)True2023-11-03['k-pop']0.7900.8720.831-4.1850.31200.0000000.07970.044011124.9871183551490
4Eslabon ArmadoElla Baila SolaFalse2023-04-28['corrido', 'corridos tumbados', 'sad sierreno', 'sierreno']0.6680.8340.758-5.1760.48300.0000190.08370.03325147.9890165671386
5Taylor SwiftCruel SummerFalse2019-08-23['pop']0.5520.5640.702-5.7070.11700.0000210.10500.15709169.9941178427499
6Metro BoominCreepin' (with The Weeknd & 21 Savage)True2022-12-02['rap']0.7150.1720.620-6.0050.41700.0000000.08220.0484197.9500221520491
7RemaCalm Down (with Selena Gomez)False2023-04-27['afrobeats', 'nigerian pop']0.7990.8110.802-5.1960.42900.0012800.17100.037111107.0081239318490
8BizarrapShakira: Bzrp Music Sessions, Vol. 53False2023-01-11['argentine hip hop', 'pop venezolano', 'trap argentino', 'trap latino', 'urbano latino']0.7780.4980.632-5.6000.27400.0000000.09150.04932122.1040218289485
9Taylor SwiftAnti-HeroFalse2022-10-21['pop']0.6370.5330.643-6.5710.13000.0000020.14200.0519497.0081200690492
artist_nametrack_nameis_explicitalbum_release_dategenresdanceabilityvalenceenergyloudnessacousticnessinstrumentalnesslivenessspeechinesskeytempomodeduration_mstime_signaturepopularity
40JiminLike CrazyFalse2023-03-24['k-pop']0.6290.3620.733-5.4450.00250.0000000.35700.04197120.0011212241493
41EminemMockingbirdTrue2004-11-12['detroit hip hop', 'hip hop', 'rap']0.6370.2540.678-3.7980.20900.0000000.15600.2660084.0391250760491
42RAYEEscapism.True2023-02-03['uk contemporary r&b', 'uk pop']0.5380.2500.742-5.3550.13800.0000470.09340.1140296.1071272373476
43Olivia RodrigovampireTrue2023-09-08['pop']0.5110.3500.532-5.7450.17700.0000000.29100.05785138.0051219724495
44Peso PlumaPRCTrue2023-06-29['corridos tumbados', 'sad sierreno']0.7840.8930.826-6.3400.09650.0000710.12300.05387138.0780184066382
45Tyler, The CreatorSee You Again (feat. Kali Uchis)True2017-07-21['hip hop', 'rap']0.5580.6200.559-9.2220.37100.0000070.10900.0959678.5581180387492
46Bad BunnyMe Porto BonitoTrue2022-05-06['reggaeton', 'trap latino', 'urbano latino']0.9110.4250.712-5.1050.09010.0000270.09330.0817192.0050178567489
47NewJeansOMGFalse2023-01-02['k-pop', 'k-pop girl group']0.8040.7390.771-4.0670.35700.0000030.10800.04339126.9560212253487
48Natanael CanoAMGTrue2023-06-30['corrido', 'corridos tumbados', 'musica mexicana', 'sad sierreno', 'sierreno']0.7720.7860.730-6.6570.15200.0001070.27400.097211136.1750174943381
49d4vdRomantic HomicideFalse2022-07-20['bedroom pop']0.5710.2160.544-10.6130.45300.0080500.32200.02996132.0521132631491